AI enhanced optimization of college physical education programs using hybrid genetic algorithms and learning based fitness evaluation

Structured physical education (PE) programs are essential for fostering students' physical development, cognitive performance, and emotional well-being in academic settings. This study introduces a novel intelligent decision-making algorithm (IDA) for the dynamic optimization and real-time assessment of college PE programs. The proposed framework synergistically integrates a hybrid genetic algorithm (GA) that is viable global optimizer with pattern search (PS) that is an adaptive local search technique. The exploration in the search domain is performed with GA while PS exploitation in a minimal computational budget is performed by the PS. The system encodes program performance data into chromosome-like representations, enabling nuanced evaluation across academic progress, health indices, and affective-psychomotor domains. A tailored vector of weight factors refines the fitness function to reflect individual learning trajectories and institutional goals. Experimental results demonstrate that the model achieves a high prediction accuracy of 98%, with quantifiable improvements of 151.13% in holistic performance, 19.63% in educational metrics, and 26.7% in psychomotor development as compared with reported results. Comparative models, including Random Forest (RF), Adaptive Neuro-Fuzzy Inference System (ANFIS), and RF regression, that achieved accuracies of 88.21%, 94.49%, and 82%, respectively. The hybrid framework maintained a mean global fitness value of 5.3451 × 10⁻¹² with an average computational time of 1357.04 s over 100 runs that is used to validate the reliability of the proposed framework. By enhancing efficiency and reducing computational complexity, this AI-driven evaluation model offers a scalable and intelligent approach for real-time optimization and policy refinement in higher education PE curriculum planning.